 Color
Grading |
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The
quality and maturity level of agricultural
products is often associated with their color. For
example, in fresh produce markets such as red
delicious apples and peaches, dark red represents
higher quality than light red. Another example is
to use color grading to determine time to market.
In general, tomatoes must be harvested while still
green. After harvesting, the fruit continue to
ripen and their color turns lighter green, then
pink, and eventually red. Due to transportation
delays, tomatoes that are already ripe (red) when
picked must be sold to local markets. Green
tomatoes can be shipped to customers over much
greater distances.
Manually separating fruits or
vegetables into different color categories is a
labor intensive task and its accuracy is not
reliable. Most existing color grading systems are
not user-friendly and do not allow the operator to
adjust grading parameters according to his/her
color perception and color preference. We have
developed a novel and robust color mapping
technique for automated color grading that
is well suited for commercial production.
The
proposed method makes it easy for a human
operator to specify and adjust color-preference
settings for different color groups representing
distinct quality grades.
The
performance of this novel color mapping method
is illustrated using tomato maturity evaluation
as an example application.
A machine vision system for processing
automation has been designed and installed for
commercial production.
Its color grading accuracy has been
well received.
This method can be easily adapted for
other food and fruit processing applications.
Future improvement includes automated
color system calibration and more advanced color
distribution analysis. We also applied this
technique to lip shape detection and analysis
application.
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Project
Sponsors:
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Datepac,
LLC, Yuma, Arizona
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Collaborators:
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Dr.
Dong Zhang, Sun Yat-Sen University, Guangzhou, China
Dr. Guangming Xiong, Beijing Institute of
Technology, Beijing, China
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Graduate Students:
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Christopher
Greco
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Publications:
- D. Zhang, D.J.
Lee, B.J. Tippetts, and K.D. Lillywhite, “Date
Maturity and Quality Evaluation Using Color
Distribution Analysis and Back Projection,"
Journal of Food Engineering, vol. 131, p.
161-169, June 2014.
- D. Zhang, D.J.
Lee, and A. Desai, “Color Back Projection
for Date Maturity Evaluation," SPIE
Electronic Imaging, Intelligent Robots and
Computer Vision XXX: Algorithms and Techniques,
vol. 9025-34, San Francisco, CA, USA, February
2-6, 2014.
- D.J.
Lee, J.K Archibald, and G.M. Xiong, “Rapid
Color Grading for Fruit Quality Evaluation
Using Direct Color Mapping,” IEEE
Transactions on Automation Science and
Engineering, vol. 8/2, p. 292-302, April 2011.
- D.J.
Lee, and J.K Archibald, “Color Image Processing
for Date Quality Evaluation,” SPIE Electronic
Imaging, Intelligent Robots and Computer Vision
XXVII: Algorithms and Techniques, vol. 7539,
75390V1-12, San Jose, CA, USA, January 17-21,
2010.
- D.J.
Lee, J.K. Archibald, Y.C. Chang, and C.R. Greco,
“Robust Color Space Conversion and Color
Distribution Analysis Techniques for Date
Maturity Evaluation,” Journal of Food
Engineering, vol. 88/3, p. 364-372, October
2008.
- D.J.
Lee, Y.C. Chang, J.K. Archibald, and C.R. Greco,
“Color Quantization and Image Analysis for
Automated Fruit Quality
Evaluation,” IEEE Conference on
Automation Science and Engineering (CASE), p.
194-199, Washington DC, USA, August 23-26, 2008.
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D.J.
Lee, "Color Space Conversion for Linear
Color Grading", Proceedings of SPIE
Intelligent Robots and Computer Vision XIX,
vol. 4197, p. 358-366, Boston, MA, USA,
November 2000.
- D.J. Lee and R.
Anbalagan, "High-speed Automated Color Sorting
Vision System,” SPIE Optical Engineering Midwest
1995, vol. 2622, p. 573-579, Chicago, IL, USA,
April 1995.
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image to view.)
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